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Active Vibration Control of Constrained Industrial Manipulators Using Piezoelectric Actuator

Mohammadi Daniali, Mohsen | 2008

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 39285 (58)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Vossoughi, Gholamreza; Boroushaki, Mehrdad
  7. Abstract:
  8. Nowadays, due to the advancement of robotic technologies and development of industrial robots, the robot manipulators are widely used for automation of various manufacturing processes, such as finishing processes. In these applications, the contact has to be made between robot end-effector and environment. Therefore, control of interaction force in the constrained manipulators is an important demand. On the other hand, due to use of gear box and belts for energy transmission in robot joints, robot manipulators have flexible joints. The vibration, generated due to the interaction force and robot joint flexibility, can deteriorate surface roughness in automated finishing processes. In order to analyze the generated vibration, the automated robotic deburring process is simulated in this thesis. A planar two-link manipulator with flexible joints is used for this simulation.
    In this thesis, active vibration control of the constrained manipulator in automated surface finishing operations is also addressed. In the active vibration control, a secondary force is generated to reduce the vibration generated by primary force. In this project, the secondary force is produced by piezoelectric actuator while the primary force is the cutting force in automated surface finishing operation. In the proposed design, the piezoelectric actuator targets workpiece instead of the robot end-effector. In fact, piezoelectric actuator compensates the vibration by repositioning the workpiece. Two different intelligent controllers, namely neuro-PID and adaptive critic-based neurofuzzy controller, are designed. These controllers can adapt themselves with different environments. The adaptive critic-based neurofuzzy controller can be used for various robot manipulators in different automated surface finishing processes with no a priori knowledge
  9. Keywords:
  10. Robotics ; Piezoelectric Actuator ; Neuro-Fuzzy Controller ; Vibration Control ; Active Control ; Machining Process ; Automation ; Deburring

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  • TITLE OF THE THESIS.pdf
  • Master Thesis
    • Chapter 1: Introduction
      • 1.1 problem statement
      • 1.2 literature review
        • 1.2.1 Robotic automation of finishing operations
          • a. robotic deburring researches
            • Figure 1.1 Cutting surface area in edge deburring process [5]
          • b. other researches
        • 1.2.2 Vibration control of robot manipulators with flexible joints
          • a. free motion
          • b. constrained motion
        • 1.2.3 Active vibration control of machine tools
          • Figure 1.2 Section view of turning system [33]
          • Figure 1.3 Schematic design of the chuck-pallet system with active vibration control elements [35]
      • 1.3 project approach
        • Figure 1.4 Proposed system configuration for a two-link manipulator in deburring process
        • Figure 1.5 Schematic diagram of vibration control of robotic deburring
    • Chapter 2: Robot Dynamics and Control
      • 2.1 introduction
        • Figure 2.1 Schematic diagram of a two-link planar manipulator [22]
      • 2.2 robot modeling
      • 2.2.1 dynamic model of two-link manipulator
        • (2-1)
        • Figure 2.2 Coordinates of the two-link manipulator
        • , (2-2)
        • (2-3)
        • (2-4)
        • (2-5)
        • (2-6)
        • (2-7)
        • (2-8)
        • (2-9)
        • (2-10)
      • 2.2.2 modeling of mechanical and electrical parts
        • Figure 2.4 Modeling of the mechanical part as a 3-mass system
        • (2-11)
        • (2-12)
        • (2-13)
        • (2-14)
        • (2-15)
        • (2-16)
      • 2.3 robot controller
        • (2-17)
        • (2-19)
        • (2-21)
      • 2.4 simulation
        • Table 2.1 Simulation conditions for two-link robot manipulator with flexible joints
          • Figure 2.6 SIMULINK . diagram of the simulation
          • Figure 2.7 DC motor . and gear subsystem
      • 2.5 simulation results
        • Figure 2.8 Robot end-effector position in x direction versus time
        • Figure 2.13 Random force exerted at the tip of the robot in y direction
    • Chapter 3: Deburring process
      • 3.1 introduction
      • 3.2 dynamic deburring model
        • 3.2.1 Cutting forces
          • (3-1)
          • (3-3)
          • (3-5)
        • 3.2.2 Dynamic-regenerative chip thickness
          • (3-7)
          • Figure 3.3 Dynamic regenerative chip thickness
          • (3-8)
          • (3-9)
          • (3-10)
          • (3-11)
          • (3-12)
        • 3.2.3 Axial depth of cut
          • (3-13)
            • (3-14)
            • (3-15)
          • (3-16)
            • (3-19)
      • 3.3 simulation
        • (3-20)
        • Figure 3.4 Block diagram of regenerative vibration in deburring process
        • Figure 3.5 Deburring model with two degree of freedom
        • Table 3.1 Simulation conditions for deburring with flexible structure
        • Figure 3.8 Burr height as a random signal
    • Chapter 4: Piezoelectric actuator
      • 4.1 introduction
      • 4.2 physical background
        • Figure 4.1 Schematic representation of the different relations in a piezoelectric actuator [41]
        • Figure 4.2 Illustration of a piezoelectric stack actuator
      • 4.3 piezoelectric actuator modeling
        • (4-1)
        • (4-2)
        • 4.3.1 Electromechanical model
          • Figure 4.3 Electromechanical model [41]
          • (4-3)
            • (4-6)
            • (4-8)
            • (4-10)
            • (4-12)
        • 4.3.2 Hysteric model
          • (4-14)
          • Figure 4.4 Realistic hysteresis loop [41]
          • (4-15)
          • (4-17)
          • (4-19)
        • 4.3.3 Mechanical model
          • Figure 4.5 Piezoelectric actuator in undeformed and deformed state
          • (4-20)
          • (4-21)
          • l= 0 (4-23)
          • l= n (4-24)
          • Figure 4.6 Chain of mass-spring-damper systems as an mechanical model of piezoelectric actuator
        • 4.3.4 Mechanical model of a piezo-actuated positioning mechanism
          • Figure 4.6 Total system of piezoactuator and mass-spring-damper system (stage)
          • (4-25)
          • (4-26)
          • (4-27)
          • (4-28)
          • Figure 4.9 Root Locus for increasing value of the stage mass ( 0 < ms < mp ) [41]
          • (4-29)
          • (4-30)
          • (4-32)
        • 4.3.5 Charge steering configuration
          • Figure 4.10 Actuator hysteresis curve for voltage steering (control) [42]
          • Figure 4.11 Actuator hysteresis curve for charge steering (control) [42]
          • Figure 4.12 Basic configuration for charge control [41]
          • (4-33)
        • 4.3.6 Total model for the case of charge control
          • (4-34)
          • (4-36)
          • (4-38)
          • (4-39)
          • (4-41)
    • Chapter 5: Intelligent controller
      • 5.1 introduction
      • 5.2 active vibration control
        • 5.2.1 Feedback control
          • Figure 5.1 The components of a feedback control system [45]
        • 5.2.2 Feedforward control
          • Figure 5.2 The components of a feedforward control system [45]
          • Figure 5.3 Active control system with filtered-reference LMS algorithm based control
          • Figure 5.5 Block diagram of feedforward active vibration control structure
          • (5-1)
          • (5-3)
          • Figure 5.6 Neuro-modeling of the inverse plant Q0-1 [47]
          • Figure 5.7 Neuro-modeling of the plant Q1 [47]
          • Figure 5.8 Training the neuro-controller [47]
      • 5.3 neuro-pid controller
        • (5-4)
        • Figure 5.9 Neuro-PID structure
        • (5-5)
        • (5-9)
        • (5-12)
        • (5-14)
        • (5-15)
      • 5.4 adaptive critic-based neurofuzzy controller
        • 5.4.1 Neurofuzzy networks [55]
          • Rj ( jth rule ) : If ( x1 is Fj1 ) and ( x2 is Fj2 ) and … and ( xn is Fjn ) Then cj = gj ( X )
          • (5-16)
          • (5-17)
          • (5-18)
          • (5-19)
          • Figure 5.9 A sample neurofuzzy structure equivalent with a MISO TSK fuzzy inference system [55]
          • Figure 5.10 Structure of adaptive critic-based neurofuzzy controller
        • 5.4.2 Learning algorithm
          • (5-20)
          • (5-21)
          • (5-22)
          • (5-23)
          • (5-24)
          • (5-25)
          • (5-26)
          • (5-27)
          • (5-28)
      • 5.5 controller design for active vibration suppression
        • (5-30)
        • Figure 5.12 The error derivative membership functions of the corresponding linguistic variables of the proposed neurofuzzy controller
        • Figure 5.14 The parameters of fuzzy inference system
        • Table 5.1 Fuzzy rules for critic
    • Chapter 6: Simulation results
      • Table 6.1 Parameter values for piezoelectric actuator
      • Table 6.2 Stage (mass, spring, and damper) parameters value
      • Figure 6.7 Block diagram of active vibration control using piezoelectric actuator
      • Figure 6.8 Overall position yo before and after applying neuro-PID controller
      • Figure 6.9 Overall position yo before and after applying adaptive critic-based neurofuzzy controller
      • Figure 6.10 Performance comparison of neuro-PID and ACNF controllers
    • References
  • paper 2
    • I. Introduction
    • II. modeling of a two-link manipulator with flexible joints
    • III. modeling of deburring process
    • IV. modeling of piezoelectric actuator
    • V. adaptive critic-based neurofuzzy structure
    • VI. simulation results
    • VII. conclusion
      • References
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